Llm

Large Language Models (LLMs) are moving from research labs into production systems at an incredible pace. As soon as organizations move beyond simple API calls to third‑party providers, a question appears: “How do we run LLMs ourselves, reliably, and at scale?” For many teams, the answer is: Kubernetes. This article dives into Kubernetes for LLMs—when it makes sense, how to design the architecture, common pitfalls, and concrete configuration examples. The focus is on inference (serving), with notes on fine‑tuning and training where relevant. ...

Introduction As large language models (LLMs) have grown into the tens and hundreds of billions of parameters, full fine‑tuning has become prohibitively expensive for most practitioners. Two techniques—LoRA and QLoRA—have emerged as leading approaches for parameter-efficient fine‑tuning (PEFT), enabling high‑quality adaptation on modest hardware. They are related but distinct: LoRA (Low-Rank Adaptation) introduces small trainable matrices on top of a frozen full‑precision model. QLoRA combines 4‑bit quantization of the base model with LoRA adapters, making it possible to fine‑tune huge models (e.g., 65B) on a single 24–48 GB GPU. This article walks through: ...

Python LLM applications often start small: a FastAPI route, a call to an LLM provider, some prompt engineering, and you’re done. Then traffic grows, latencies spike, and your CPUs sit mostly idle while users wait seconds—or tens of seconds—for responses. What went wrong? One of the most common and least understood culprits is thread pool starvation. This article explains what thread pool starvation is, why it’s especially dangerous in Python LLM apps, how to detect it, and concrete patterns to avoid or fix it. ...

Context engineering is the systematic discipline of selecting, structuring, and delivering optimal context to large language models (LLMs) to maximize reliability, accuracy, and performance—far beyond basic prompt engineering.[1][2] This zero-to-hero tutorial equips developers with foundational concepts, advanced strategies, practical Python implementations using Hugging Face Transformers and LangChain, best practices, pitfalls, and curated resources to build production-ready LLM systems.[1][7] What is Context Engineering? Context engineering treats the LLM’s context window—its limited “working memory” (typically 4K–128K+ tokens)—as a critical resource to be architected like a database or API pipeline.[2][5] It involves curating prompts, retrievals, memory, tools, and history to ensure the model receives the right information at the right time, enabling plausible task completion without hallucinations or drift.[1][4][6] ...

Large Language Models (LLMs) excel at processing text, but they lack real-time web access. By integrating browsers, developers can empower LLMs to fetch live data, automate tasks, and interact dynamically with websites. This zero-to-hero tutorial covers core methods—browser APIs, web scraping, automation, and agent pipelines—with practical Python/JS examples using tools like LangChain, Playwright, Selenium, and more. Why Browsers + LLMs? Key Use Cases Browsers bridge LLMs’ knowledge gaps by enabling: ...